Phoneme recognition using an adaptive supervised manifold learning algorithm

To effectively handle speech data lying on a nonlinear manifold embedded in a high-dimensional acoustic space, in this paper, an adaptive supervised manifold learning algorithm based on locally linear embedding (LLE) for nonlinear dimensionality reduction is proposed to extract the low-dimensional embedded data representations for phoneme recognition. The proposed method aims to make the interclass dissimilarity maximized, while the intraclass dissimilarity minimized in order to promote the discriminating power and generalization ability of the low-dimensional embedded data representations. The performance of the proposed method is compared with five well-known dimensionality reduction methods, i.e., principal component analysis, linear discriminant analysis, isometric mapping (Isomap), LLE as well as the original supervised LLE. Experimental results on three benchmarking speech databases, i.e., the Deterding database, the DARPA TIMIT database, and the ISOLET E-set database, demonstrate that the proposed method obtains promising performance on the phoneme recognition task, outperforming the other used methods.     

改进的有监督的局部线性嵌入算法及实验演示

流形学习方法中的LLE算法可以将高维数据在保持局部邻域结构的条件下降维到低维流形子空间中．并得到与原样本集具有相似局部结构的嵌入向量集合。LLE算法在数据降维处理过程中没有考虑样本的分类信息。针对这些问题进行研究,提出改进的有监督的局部线性嵌人算法（MSLLE）,并利用MatLab对该改进算法的实现效果同LLE进行实验演示比较。通过实验演示表明,MSLLE算法较LLE算法可以有利于保持数据点本身内部结构。     

基于直接估计梯度思想的数据降维算法

高维非线性数据的降维处理对于计算机完成高复杂度的数据源分析是非常重要的。从拓扑学角度分析,维数约简的过程是挖掘嵌入在高维数据中的低维线性或非线性的流形。该文在局部嵌入思想的流形学习算法的基础上,提出直接估计梯度值的方法,从而达到局部线性误差逼近最小化,实现高维非线性数据的维数约简,并在Swiss roll曲线上采样测试取得了良好的降维效果。     

Discriminant locally linear embedding with high-order tensor data.

Graph-embedding along with its linearization and kernelization provides a general framework that unifies most traditional dimensionality reduction algorithms. From this framework, we propose a new manifold learning technique called discriminant locally linear embedding (DLLE), in which the local geometric properties within each class are preserved according to the locally linear embedding (LLE) criterion, and the separability between different classes is enforced by maximizing margins between point pairs on different classes. To deal with the out-of-sample problem in visual recognition with vector input, the linear version of DLLE, i.e., linearization of DLLE (DLLE/L), is directly proposed through the graph-embedding framework. Moreover, we propose its multilinear version, i.e., tensorization of DLLE, for the out-of-sample problem with high-order tensor input. Based on DLLE, a procedure for gait recognition is described. We conduct comprehensive experiments on both gait and face recognition, and observe that: 1) DLLE along its linearization and tensorization outperforms the related versions of linear discriminant analysis, and DLLE/L demonstrates greater effectiveness than the linearization of LLE; 2) algorithms based on tensor representations are generally superior to linear algorithms when dealing with intrinsically high-order data; and 3) for human gait recognition, DLLE/L generally obtains higher accuracy than state-of-the-art gait recognition algorithms on the standard University of South Florida gait database.     

基于彩色人脸图像的信息融合与识别方法

图像的彩色信息进行图像识别并有效地降低因利用颜色信息所带来的计算量大幅增加问题,提出了一种基于彩色图像的监督近邻保留嵌套的人脸识别方法,通过对图像的彩色信息进行信息融合并利用监督近邻保留嵌套算法来提高人脸识别的效率。首先,采用Gabor变换分别对彩色图像的每个彩色分量图提取Gabor特征;然后采用典型相关分析对所提取的Gabor特征进行特征融合,并采用监督近邻保留嵌套算法对高维彩色图像特征进行降维;最后,采用最近邻分类器对图像进行分类。实验基于XM2VTS和FRAV2D彩色人脸数据库,采用主成分分析、线性判别分析以及监督近邻保留嵌套对基于灰度图像的Gabor特征和基于彩色信息融合的Gabor特征进行降维,其结果说明多信通彩色图像融合技术与监督近邻保留嵌套结合的方法可以显著提高识别系统性能。     

子空间半监督Fisher判别分析

Fisher判别分析寻找一个使样本数据类间散度与样本数据类内散度比值最大的子空间, 是一种很流行的监督式特征降维方法. 标注样本数据所属的类别通常需要大量的人工, 消耗大量的时间, 付出昂贵的成本. 为了解决同时利用有类别信息的样本数据和没有类别信息的样本数据用于寻找降维子空间的问题, 我们提出了一种子空间半监督Fisher判别分析方法. 子空间半监督Fisher判别分析寻找这样一个子空间, 这个子空间即保留了从有类别信息的样本数据中学习的类别判别结构, 也保留了从有类别信息的样本数据和没有类别信息的样本数据中学习的样本结构信息. 我们还推导了基于核的子空间半监督Fisher判别分析方法. 通过人脸识别实验验证了本文算法的有效性.     

Locality preserving embedding for face and handwriting digital recognition

Most supervised manifold learning-based methods preserve the original neighbor relationships to pursue the discriminating power. Thus, structure information of the data distributions might be neglected and destroyed in low-dimensional space in a certain sense. In this paper, a novel supervised method, called locality preserving embedding (LPE), is proposed to feature extraction and dimensionality reduction. LPE can give a low-dimensional embedding for discriminative multi-class sub-manifolds and preserves principal structure information of the local sub-manifolds. In LPE framework, supervised and unsupervised ideas are combined together to learn the optimal discriminant projections. On the one hand, the class information is taken into account to characterize the compactness of local sub-manifolds and the separability of different sub-manifolds. On the other hand, at the same time, all the samples in the local neighborhood are used to characterize the original data distributions and preserve the structure in low-dimensional subspace. The most significant difference from existing methods is that LPE takes the distribution directions of local neighbor data into account and preserves them in low-dimensional subspace instead of only preserving the each local sub-manifold’s original neighbor relationships. Therefore, LPE optimally preserves both the local sub-manifold’s original neighborhood relationships and the distribution direction of local neighbor data to separate different sub-manifolds as far as possible. The criterion, similar to the classical Fisher criterion, is a Rayleigh quotient in form, and the optimal linear projections are obtained by solving a generalized Eigen equation. Furthermore, the framework can be directly used in semi-supervised learning, and the semi-supervised LPE and semi-supervised kernel LPE are given. The proposed LPE is applied to face recognition (on the ORL and Yale face databases) and handwriting digital recognition (on the USPS database). The experimental results show that LPE consistently outperforms classical linear methods, e.g., principal component analysis and linear discriminant analysis, and the recent manifold learning-based methods, e.g., marginal Fisher analysis and constrained maximum variance mapping.     

Guided Locally Linear Embedding

Nonlinear dimensionality reduction is the problem of retrieving a low-dimensional representation of a manifold that is embedded in a high-dimensional observation space. Locally Linear Embedding (LLE), a prominent dimensionality reduction technique is an unsupervised algorithm; as such, it is not possible to guide it toward modes of variability that may be of particular interest. This paper proposes a supervised variation of LLE. Similar to LLE, it retrieves a low-dimensional global coordinate system that faithfully represents the embedded manifold. Unlike LLE, however, it produces an embedding in which predefined modes of variation are preserved. This can improve several supervised learning tasks including pattern recognition, regression, and data visualization.     

中心近邻嵌入学习算法的人脸识别研究

针对人脸识别问题,提出了一种中心近邻嵌入的学习算法,其与经典的局部线性嵌入和保局映射不同,它是一种有监督的线性降维方法。该方法首先通过计算各类样本中心,并引入中心近邻距离代替两样本点之间的直接距离作为权系数函数的输入;然后再保持中心近邻的几何结构不变的情况下把高维数据嵌入到低维坐标系中。通过中心近邻嵌入学习算法与其他3种人脸识别方法(即主成分分析、线形判别分析及保局映射)在ORL、Yale及UMIST人脸库上进行的比较实验结果表明,它在高维数据低维可视化和人脸识别效果等方面均较其他3种方法取得了更好的效果。     

Linear discriminant projection embedding based on patches alignment

Dimensionality reduction is often required as a preliminary stage in many data analysis applications. In this paper, we propose a novel supervised dimensionality reduction method, called linear discriminant projection embedding (LDPE), for pattern recognition. LDPE first chooses a set of overlapping patches which cover all data points using a minimum set cover algorithm with geodesic distance constraint. Then, principal component analysis (PCA) is applied on each patch to obtain the data's local representations. Finally, patches alignment technique combined with modified maximum margin criterion (MMC) is used to yield the discriminant global embedding. LDPE takes both label information and structure of manifold into account, thus it can maximize the dissimilarities between different classes and preserve data's intrinsic structures simultaneously. The efficiency of the proposed algorithm is demonstrated by extensive experiments using three standard face databases (ORL, YALE and CMU PIE). Experimental results show that LDPE outperforms other classical and state of art algorithms.     

A unified framework for semi-supervised dimensionality reduction

In practice, many applications require a dimensionality reduction method to deal with the partially labeled problem. In this paper, we propose a semi-supervised dimensionality reduction framework, which can efficiently handle the unlabeled data. Under the framework, several classical methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), maximum margin criterion (MMC), locality preserving projections (LPP) and their corresponding kernel versions can be seen as special cases. For high-dimensional data, we can give a low-dimensional embedding result for both discriminating multi-class sub-manifolds and preserving local manifold structure. Experiments show that our algorithms can significantly improve the accuracy rates of the corresponding supervised and unsupervised approaches.     

A novel maximum margin neighborhood preserving embedding for face recognition

This paper presents a novel supervised linear dimensionality reduction approach called maximum margin neighborhood preserving embedding (MMNPE). The central idea is to modify the neighborhood preserving embedding by maximizing the maximum margin distance while preserving the geometric structure of the manifold. Experimental results conducted on the ORL database, the Yale database and the VALID face database indicate the effectiveness of the proposed MMNPE.     

A comparative study of nonlinear manifold learning methods for cancer microarray data classification

The paper presents an empirical comparison of the most prominent nonlinear manifold learning techniques for dimensionality reduction in the context of high-dimensional microarray data classification. In particular, we assessed the performance of six methods: isometric feature mapping, locally linear embedding, Laplacian eigenmaps, Hessian eigenmaps, local tangent space alignment and maximum variance unfolding. Unlike previous studies on the subject, the experimental framework adopted in this work properly extends to dimensionality reduction the supervised learning paradigm, by regarding the test set as an out-of-sample set of new points which are excluded from the manifold learning process. This in order to avoid a possible overestimate of the classification accuracy which may yield misleading comparative results. The different empirical approach requires the use of a fast and effective out-of-sample embedding method for mapping new high-dimensional data points into an existing reduced space. To this aim we propose to apply multi-output kernel ridge regression, an extension of linear ridge regression based on kernel functions which has been recently presented as a powerful method for out-of-sample projection when combined with a variant of isometric feature mapping. Computational experiments on a wide collection of cancer microarray data sets show that classifiers based on Isomap, LLE and LE were consistently more accurate than those relying on HE, LTSA and MVU. In particular, under different experimental conditions LLE-based classifier emerged as the most effective method whereas Isomap algorithm turned out to be the second best alternative for dimensionality reduction.     

Modified locally linear discriminant embedding for plant leaf recognition

Based on locally linear embedding (LLE) and modified maximizing margin criterion (MMMC), a modified locally linear discriminant embedding (MLLDE) algorithm is proposed for plant leaf recognition in this paper. By MLLDE, the plant leaf images are mapped into a leaf subspace for analysis, which can detect the essential leaf manifold structure. Furthermore, the unwanted variations resulting from changes in period, location, and illumination can be eliminated or reduced. Different from principal component analysis (PCA) and linear discriminant analysis (LDA), which can only deal with flat Euclidean structures of plant leaf space, MLLDE not only inherits the advantages of locally linear embedding (LLE), but makes full use of class information to improve discriminant power by introducing translation and rescaling models. The experimental results on real plant leaf database show that the MLLDE is effective for plant leaf recognition.     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

Locality preserving and global discriminant projection with prior information

Existing supervised and semi-supervised dimensionality reduction methods utilize training data only with class labels being associated to the data samples for classification. In this paper, we present a new algorithm called locality preserving and global discriminant projection with prior information (LPGDP) for dimensionality reduction and classification, by considering both the manifold structure and the prior information, where the prior information includes not only the class label but also the misclassification of marginal samples. In the LPGDP algorithm, the overlap among the class-specific manifolds is discriminated by a global class graph, and a locality preserving criterion is employed to obtain the projections that best preserve the within-class local structures. The feasibility of the LPGDP algorithm has been evaluated in face recognition, object categorization and handwritten Chinese character recognition experiments. Experiment results show the superior performance of data modeling and classification to other techniques, such as linear discriminant analysis, locality preserving projection, discriminant locality preserving projection and marginal Fisher analysis.     

小波域能量谱和非线性降维的病理嗓音识别

提出了一种病理嗓音小波域建模分析方法并对病理嗓音进行识别。首先对病理嗓音进行多尺度连续小波变换时频分析,然后对沿尺度轴方向的能量谱进行高斯混合建模,采用最大似然估计方法求解得到模型统计学参数作为特征参数,并且使用改进的动态加权局部线性嵌入方法对特征参数进行非线性降维处理。实验结果表明,小波域能量谱特征经过非线性降维后对病理嗓音的识别率达到97.45%,改进的动态加权局部线性嵌入方法降维效果优于主成分分析和局部线性嵌入方法。     

核岭回归的邻域保持最大间隔分析的人脸识别

邻域保持嵌入是局部线性嵌入的线性近似,强调保持数据流形的局部结构.改进的最大间隔准则重视数据流形的判别和几何结构,提高了对数据的分类性能.文中提出的核岭回归的邻域保持最大间隔分析既保持流形的局部结构,又使不同类别的数据保持最大间隔,以此构建算法的目标函数.为了解决数据流形高度非线性化的问题,算法采用核岭回归计算特征空间的变换矩阵.先求解数据样本在核子空间中降维映射的结果,再解得核子空间.在标准人脸数据库上的实验表明该算法正确有效,并且识别性能优于普通的流形学习算法.     

Kernel-view based discriminant approach for embedded feature extraction in high-dimensional space

Derived from the traditional manifold learning algorithms, local discriminant analysis methods identify the underlying submanifold structures while employing discriminative information for dimensionality reduction. Mathematically, they can all be unified into a graph embedding framework with different construction criteria. However, such learning algorithms are limited by the curse-of-dimensionality if the original data lie on the high-dimensional manifold. Different from the existing algorithms, we consider the discriminant embedding as a kernel analysis approach in the sample space, and a kernel-view based discriminant method is proposed for the embedded feature extraction, where both PCA pre-processing and the pruning of data can be avoided. Extensive experiments on the high-dimensional data sets show the robustness and outstanding performance of our proposed method.     

基于随机游走扩散映射的降维算法

传统数据降维算法分为线性或流形学习降维算法,但在实际应用中很难确定需要哪一类算法.设计一种综合的数据降维算法,以保证它的线性降维效果下限为主成分分析方法且在流形学习降维方面能揭示流形的数据结构.通过对高维数据构造马尔可夫转移矩阵,使越相似的节点转移概率越大,从而发现高维数据降维到低维流形的映射关系.实验结果表明,在人造...